Kubernetes v1.16
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Jobs - Run to Completion

A Job creates one or more Pods and ensures that a specified number of them successfully terminate.
As pods successfully complete, the Job tracks the successful completions. When a specified number
of successful completions is reached, the task (ie, Job) is complete. Deleting a Job will clean up
the Pods it created.

A simple case is to create one Job object in order to reliably run one Pod to completion.
The Job object will start a new Pod if the first Pod fails or is deleted (for example
due to a node hardware failure or a node reboot).

the Job represents the overall task, and is complete when there is one successful Pod for each value in the range 1 to .spec.completions.

not implemented yet: Each Pod is passed a different index in the range 1 to .spec.completions.

Parallel Jobs with a work queue:

do not specify .spec.completions, default to .spec.parallelism.

the Pods must coordinate amongst themselves or an external service to determine what each should work on. For example, a Pod might fetch a batch of up to N items from the work queue.

each Pod is independently capable of determining whether or not all its peers are done, and thus that the entire Job is done.

when any Pod from the Job terminates with success, no new Pods are created.

once at least one Pod has terminated with success and all Pods are terminated, then the Job is completed with success.

once any Pod has exited with success, no other Pod should still be doing any work for this task or writing any output. They should all be in the process of exiting.

For a non-parallel Job, you can leave both .spec.completions and .spec.parallelism unset. When both are
unset, both are defaulted to 1.

For a fixed completion count Job, you should set .spec.completions to the number of completions needed.
You can set .spec.parallelism, or leave it unset and it will default to 1.

For a work queue Job, you must leave .spec.completions unset, and set .spec.parallelism to
a non-negative integer.

For more information about how to make use of the different types of job, see the job patterns section.

Controlling Parallelism

The requested parallelism (.spec.parallelism) can be set to any non-negative value.
If it is unspecified, it defaults to 1.
If it is specified as 0, then the Job is effectively paused until it is increased.

Actual parallelism (number of pods running at any instant) may be more or less than requested
parallelism, for a variety of reasons:

For fixed completion count Jobs, the actual number of pods running in parallel will not exceed the number of
remaining completions. Higher values of .spec.parallelism are effectively ignored.

For work queue Jobs, no new Pods are started after any Pod has succeeded – remaining Pods are allowed to complete, however.

If the Job controller failed to create Pods for any reason (lack of ResourceQuota, lack of permission, etc.),
then there may be fewer pods than requested.

The Job controller may throttle new Pod creation due to excessive previous pod failures in the same Job.

When a Pod is gracefully shut down, it takes time to stop.

Handling Pod and Container Failures

A container in a Pod may fail for a number of reasons, such as because the process in it exited with
a non-zero exit code, or the container was killed for exceeding a memory limit, etc. If this
happens, and the .spec.template.spec.restartPolicy = "OnFailure", then the Pod stays
on the node, but the container is re-run. Therefore, your program needs to handle the case when it is
restarted locally, or else specify .spec.template.spec.restartPolicy = "Never".
See pod lifecycle for more information on restartPolicy.

An entire Pod can also fail, for a number of reasons, such as when the pod is kicked off the node
(node is upgraded, rebooted, deleted, etc.), or if a container of the Pod fails and the
.spec.template.spec.restartPolicy = "Never". When a Pod fails, then the Job controller
starts a new Pod. This means that your application needs to handle the case when it is restarted in a new
pod. In particular, it needs to handle temporary files, locks, incomplete output and the like
caused by previous runs.

Note that even if you specify .spec.parallelism = 1 and .spec.completions = 1 and
.spec.template.spec.restartPolicy = "Never", the same program may
sometimes be started twice.

If you do specify .spec.parallelism and .spec.completions both greater than 1, then there may be
multiple pods running at once. Therefore, your pods must also be tolerant of concurrency.

Pod backoff failure policy

There are situations where you want to fail a Job after some amount of retries
due to a logical error in configuration etc.
To do so, set .spec.backoffLimit to specify the number of retries before
considering a Job as failed. The back-off limit is set by default to 6. Failed
Pods associated with the Job are recreated by the Job controller with an
exponential back-off delay (10s, 20s, 40s …) capped at six minutes. The
back-off count is reset if no new failed Pods appear before the Job’s next
status check.

Note: Issue #54870 still exists for versions of Kubernetes prior to version 1.12

Note: If your job has restartPolicy = "OnFailure", keep in mind that your container running the Job
will be terminated once the job backoff limit has been reached. This can make debugging the Job’s executable more difficult. We suggest setting
restartPolicy = "Never" when debugging the Job or using a logging system to ensure output
from failed Jobs is not lost inadvertently.

Job Termination and Cleanup

When a Job completes, no more Pods are created, but the Pods are not deleted either. Keeping them around
allows you to still view the logs of completed pods to check for errors, warnings, or other diagnostic output.
The job object also remains after it is completed so that you can view its status. It is up to the user to delete
old jobs after noting their status. Delete the job with kubectl (e.g. kubectl delete jobs/pi or kubectl delete -f ./job.yaml). When you delete the job using kubectl, all the pods it created are deleted too.

By default, a Job will run uninterrupted unless a Pod fails (restartPolicy=Never) or a Container exits in error (restartPolicy=OnFailure), at which point the Job defers to the
.spec.backoffLimit described above. Once .spec.backoffLimit has been reached the Job will be marked as failed and any running Pods will be terminated.

Another way to terminate a Job is by setting an active deadline.
Do this by setting the .spec.activeDeadlineSeconds field of the Job to a number of seconds.
The activeDeadlineSeconds applies to the duration of the job, no matter how many Pods are created.
Once a Job reaches activeDeadlineSeconds, all of its running Pods are terminated and the Job status will become type: Failed with reason: DeadlineExceeded.

Note that a Job’s .spec.activeDeadlineSeconds takes precedence over its .spec.backoffLimit. Therefore, a Job that is retrying one or more failed Pods will not deploy additional Pods once it reaches the time limit specified by activeDeadlineSeconds, even if the backoffLimit is not yet reached.

Note that both the Job spec and the Pod template spec within the Job have an activeDeadlineSeconds field. Ensure that you set this field at the proper level.

Clean Up Finished Jobs Automatically

Finished Jobs are usually no longer needed in the system. Keeping them around in
the system will put pressure on the API server. If the Jobs are managed directly
by a higher level controller, such as
CronJobs, the Jobs can be
cleaned up by CronJobs based on the specified capacity-based cleanup policy.

TTL Mechanism for Finished Jobs

Might be buggy. Enabling the feature may expose bugs. Disabled by default.

Support for feature may be dropped at any time without notice.

The API may change in incompatible ways in a later software release without notice.

Recommended for use only in short-lived testing clusters, due to increased risk of bugs and lack of long-term support.

Another way to clean up finished Jobs (either Complete or Failed)
automatically is to use a TTL mechanism provided by a
TTL controller for
finished resources, by specifying the .spec.ttlSecondsAfterFinished field of
the Job.

When the TTL controller cleans up the Job, it will delete the Job cascadingly,
i.e. delete its dependent objects, such as Pods, together with the Job. Note
that when the Job is deleted, its lifecycle guarantees, such as finalizers, will
be honored.

The Job pi-with-ttl will be eligible to be automatically deleted, 100
seconds after it finishes.

If the field is set to 0, the Job will be eligible to be automatically deleted
immediately after it finishes. If the field is unset, this Job won’t be cleaned
up by the TTL controller after it finishes.

Note that this TTL mechanism is alpha, with feature gate TTLAfterFinished. For
more information, see the documentation for
TTL controller for
finished resources.

Job Patterns

The Job object can be used to support reliable parallel execution of Pods. The Job object is not
designed to support closely-communicating parallel processes, as commonly found in scientific
computing. It does support parallel processing of a set of independent but related work items.
These might be emails to be sent, frames to be rendered, files to be transcoded, ranges of keys in a
NoSQL database to scan, and so on.

In a complex system, there may be multiple different sets of work items. Here we are just
considering one set of work items that the user wants to manage together — a batch job.

There are several different patterns for parallel computation, each with strengths and weaknesses.
The tradeoffs are:

One Job object for each work item, vs. a single Job object for all work items. The latter is
better for large numbers of work items. The former creates some overhead for the user and for the
system to manage large numbers of Job objects.

Number of pods created equals number of work items, vs. each Pod can process multiple work items.
The former typically requires less modification to existing code and containers. The latter
is better for large numbers of work items, for similar reasons to the previous bullet.

Several approaches use a work queue. This requires running a queue service,
and modifications to the existing program or container to make it use the work queue.
Other approaches are easier to adapt to an existing containerised application.

The tradeoffs are summarized here, with columns 2 to 4 corresponding to the above tradeoffs.
The pattern names are also links to examples and more detailed description.

When you specify completions with .spec.completions, each Pod created by the Job controller
has an identical spec. This means that
all pods for a task will have the same command line and the same
image, the same volumes, and (almost) the same environment variables. These patterns
are different ways to arrange for pods to work on different things.

This table shows the required settings for .spec.parallelism and .spec.completions for each of the patterns.
Here, W is the number of work items.

Advanced Usage

Specifying your own pod selector

Normally, when you create a Job object, you do not specify .spec.selector.
The system defaulting logic adds this field when the Job is created.
It picks a selector value that will not overlap with any other jobs.

However, in some cases, you might need to override this automatically set selector.
To do this, you can specify the .spec.selector of the Job.

Be very careful when doing this. If you specify a label selector which is not
unique to the pods of that Job, and which matches unrelated Pods, then pods of the unrelated
job may be deleted, or this Job may count other Pods as completing it, or one or both
Jobs may refuse to create Pods or run to completion. If a non-unique selector is
chosen, then other controllers (e.g. ReplicationController) and their Pods may behave
in unpredictable ways too. Kubernetes will not stop you from making a mistake when
specifying .spec.selector.

Here is an example of a case when you might want to use this feature.

Say Job old is already running. You want existing Pods
to keep running, but you want the rest of the Pods it creates
to use a different pod template and for the Job to have a new name.
You cannot update the Job because these fields are not updatable.
Therefore, you delete Job old but leave its pods
running, using kubectl delete jobs/old --cascade=false.
Before deleting it, you make a note of what selector it uses:

Then you create a new Job with name new and you explicitly specify the same selector.
Since the existing Pods have label controller-uid=a8f3d00d-c6d2-11e5-9f87-42010af00002,
they are controlled by Job new as well.

You need to specify manualSelector: true in the new Job since you are not using
the selector that the system normally generates for you automatically.

The new Job itself will have a different uid from a8f3d00d-c6d2-11e5-9f87-42010af00002. Setting
manualSelector: true tells the system to that you know what you are doing and to allow this
mismatch.

Alternatives

Bare Pods

When the node that a Pod is running on reboots or fails, the pod is terminated
and will not be restarted. However, a Job will create new Pods to replace terminated ones.
For this reason, we recommend that you use a Job rather than a bare Pod, even if your application
requires only a single Pod.

Replication Controller

Jobs are complementary to Replication Controllers.
A Replication Controller manages Pods which are not expected to terminate (e.g. web servers), and a Job
manages Pods that are expected to terminate (e.g. batch tasks).

As discussed in Pod Lifecycle, Job is only appropriate
for pods with RestartPolicy equal to OnFailure or Never.
(Note: If RestartPolicy is not set, the default value is Always.)

Single Job starts Controller Pod

Another pattern is for a single Job to create a Pod which then creates other Pods, acting as a sort
of custom controller for those Pods. This allows the most flexibility, but may be somewhat
complicated to get started with and offers less integration with Kubernetes.

One example of this pattern would be a Job which starts a Pod which runs a script that in turn
starts a Spark master controller (see spark example), runs a spark
driver, and then cleans up.

An advantage of this approach is that the overall process gets the completion guarantee of a Job
object, but complete control over what Pods are created and how work is assigned to them.

Cron Jobs

You can use a CronJob to create a Job that will run at specified times/dates, similar to the Unix tool cron.